code: def process_predicted(df, col_name):
      """Transform the TFT predicted data into Qlib format.
  
      Args:
        df: Original DataFrame.
        fillna: New column name.
  
      Returns:
        Transformed DataFrame.
  
      """
      df_res = df.copy()
      df_res = df_res.rename(
          columns={
              "forecast_time": "datetime",
              "identifier": "instrument",
              "t+4": col_name,
          }
      )
      df_res = df_res.set_index(["datetime", "instrument"]).sort_index()
      df_res = df_res[[col_name]]
      return df_res
  
imports: from pathlib import Path
  from typing import Union
  import numpy
  import pandas
  import tensorflow.compat.v1
  import data_formatters.base
  import expt_settings.configs
  import libs.hyperparam_opt
  import libs.tft_model
  import libs.utils
  import os
  import datetime
  from qlib.model.base import ModelFT
  from qlib.data.dataset import DatasetH
  from qlib.data.dataset.handler import DataHandlerLP
label_desc: Use image node_id 4 for calling a global function with example usage: process_predicted(df, col_name) and returns: df_res